Improving TV user experiences is a key problem in consumer electronics production and multimedia applications development. With the emergence of many forms of smart TVs, the current TV systems are experiencing significant in-and-out upgrading, from hardware, firmware, software framework, applications, to user experiences. Many new trials have been introduced to enhance users' interaction with TV, for example, by providing a keyboard as a part of the remote control; and another proposal is a motion-sensor based remote control to enable TV users to point and even drag and drop an icon on the TV screen. People start to debate whether a lean-back (passive) or a lean-forward (active) usage model better fits user TV experiences. On the other hand, the cloud computing era is coming, and many web-based cloud services, for example, mapping and video sharing services, have been introduced to the TV world to further enhance the system's capability and smartness.
There is a recent statistical report showing that in the US, people on average watch 4 hours of TV per day, however, 86% of the TV viewers have no idea what to watch when they sit in front of the TV. If each TV is sufficiently smart to recognize the viewer (or viewer group), understand the viewer's preferences, and recommend the right content for the viewer to watch, then a certain kind of user experience can be brought to the TV viewer. Many companies and research organizations have made efforts in this area for the past decade, and as a consequence, electronics program guide (EPG) and TV recommendation systems are current trends in the field. The results can be seen in product proposals such as TV3P, AVATAR, and queveo.tv, and in concepts such as the Semantic Web and PDPR DTV.
These exemplary efforts are incorporated herein by reference via exemplary works: L. Ardissono, F. Portis, and P. Torasso, “Architecture of a system for the generation of personalized Electronics Program Guide”, 2001; Z. Yu, X. Zhou, “TV3P: An Adaptive Assistant for Personalized TV”, IEEE Trans. Consumer Electronics, Vol. 50, No. 1, February 2004, pp. 393-399; Y. B. Fernandez, J. J. P. Arias, M. L. Nores, A. G. Solla, and M. R. Cabrer, “AVATAR: An Improved Solution for Personalized TV based on Semantic Inference”, IEEE Trans. Consumer Electronics, Vol. 52, No. 1, February 2006, pp. 223-231; A. B. B. Martinez, J. J. P. Arias, A. F. Vilas, J. G. Duque, and M. L. Nores, “What's on TV Tonight? An Efficient and Effective Personalized Recommender System of TV Programs”, IEEE Trans. Consumer Electronics, Vol. 55, No. 1, February 2009. pp. 286-294; C. Shin and W. Woo, “Socially Aware TV Program Recommender for Multiple Viewers”, IEEE Trans. Consumer Electronics, Vol. 55, No. 2, May 2009, pp. 927-932; and S. Lee, D. Lee, S. Lee, “Personalized DTV Program Recommendation System under a Cloud Computing Environment”, IEEE Trans. Consumer Electronics, Vol. 56, No. 2, May 2010, pp. 1034-1042.
Although many TV recommendation systems have been proposed to improve user experiences, the efforts were undermined by a superficially concluded user preferences investigation. The efforts were also limited by a lack of control on the endpoint devices or return channels from the endpoints to the content/service provider. Some systems provide users the capability to convey explicit feedback during their watching experience, which can seem annoying or distracting for users if embedded in content. Another way to provide feedback is to provide specific buttons, for example, like/dislike, on the remote control to collect users' feedback. Yet another way is implicit user feedback collection, which includes deriving the viewers' tastes from their program selection history, using viewing time as an indication of user preferences, and so on.
These feedback collection methods and systems are incorporated herein by reference via exemplary works: M. Bjelica, “Towards TV Recommender System: Experimental with User Modeling”, IEEE Trans. Consumer Electronics, Vol. 56, No. 3, August 2010, pp. 1763-1769; W. P. Lee, and J. H. Wang, “A User-Centered Remote Control System for Personalized Multimedia Channel Selection”, IEEE Trans. Consumer Electronics, Vol. 50, No. 4, November 2004, pp. 1009-1015; T. Isobe, M. Fujiwara, H. Kaneta, T. Morita, and N. Uratani, “Development of a TV Reception Navigation System Personalized with Viewing Habits”, IEEE Trans. Consumer Electronics, Vol. 51, No. 2, May 2005, pp. 665-674; and J. Parsons, P. Ralph, and K. Gallagher, “Using viewing time to infer user preference in recommender systems”, in Proc. AAAI Workshop on Semantic Web Personalization (SWP-04), pp. 52-64, 2004.
In an effort to provide viewers preferred content for selection, Applicant proposes a system and method for presenting content in a format that minimizes the amount of remote control use needed to select desirable content.